Search Results (1,088)

Background: Fundamental movement skills (FMS) underpin lifelong physical activity (PA) and health, yet many children are failing to meet age-appropriate standards. Caregivers hold a critical influence over children’s motor development, but little is known about what helps or hinders family participation, including messaging. This study explored the determinants of family FMS engagement in the United Kingdom (UK) during early childhood, addressing unexplored gaps in how guidance reaches families and the role of grandparents in supporting children’s motor development. Methods: Twenty-three semi-structured interviews were conducted with 15 caregivers and 8 educators, including 4 grandparents and 2 family hub practitioners who offered original insights. Eleven children aged 3–5 years completed a flexible draw-and-tell task, enabling inclusion of rarely represented 3-year-olds. Thematic analysis was deployed. Results: Families and outdoor spaces were pivotal to children’s movement opportunities. However, awareness and understanding of FMS and UK PA guidance were poor, even among educators, disrupting dissemination of information to families. Greater emphasis on PA and FMS concepts within professional development, alongside clearer signposting to resources, more visible public-facing campaigns, and digital formats, could improve how families receive these messages. Tensions emerged between parents’ concerns about grandparents’ physical capability and grandparents’ belief that they could adapt to support children’s development. Unexpectedly, no children drew technology despite screen time frequently displacing active play, hinting at its normalisation and regulatory role in children’s lives. Conclusions: To enhance family understanding, value, and participation in FMS, UK policy must evolve to become more visible, relatable, and responsive to diverse family needs. Full article

Wearable neurotechnology depends critically on continuous movement monitoring to characterize motor impairment and recovery in real-world settings. While joint torque serves as a clinically essential kinetic marker, estimating it directly on-device from inertial signals remains challenging due to stringent computational, memory, and energy constraints. Lightweight pipelines typically omit computationally expensive time–frequency processing; however, this omission degrades the observability of dynamics encoded in 1D IMU signals and diminishes the effectiveness of standard knowledge distillation strategies. To enable reliable on-device torque inference, we propose a Physically Guided Dual-Consistency Knowledge Distillation (PDC-KD) framework that explicitly integrates biomechanical priors into the learning process through two collaborative pathways: parameter-manifold alignment and physics-guided compensation. The student network receives guidance through Fisher-information-weighted parameter transfer, ensuring robust knowledge distillation despite significant model capacity mismatch. Furthermore, the framework incorporates a physics-guided regularization term that enforces dynamically consistent torque trajectories via a numerically stable Cholesky-parameterized constraint. Experiments demonstrate that the student model preserves teacher-level predictive accuracy while operating within the stringent resource constraints of edge devices (achieving a 98% parameter reduction, ∼2× faster inference, and ∼1 ms latency). Moreover, the proposed method yields torque estimates with enhanced dynamical consistency, providing an efficient biosignal-processing solution for wearable neurotechnology platforms demanding real-time movement analytics. Full article

The electrification of urban and industrial transport is driving the need for propulsion architectures that combine energy efficiency, operational flexibility and regulatory compliance. However, current electric platforms often lack the adaptability required for customized body configurations and multistage manufacturing, and their approval is hindered by the complexity of meeting electrical safety and electromagnetic compatibility (EMC) requirements at vehicle level. This article presents the conceptual design of a modular electric propulsion module developed within the MODULe project, in which the traction motor, inverter, battery pack, Battery Management System (BMS) and cooling circuits are integrated into a standardized module conceived as an Independent Technical Unit (ITU). The propulsion module dimensioned using a modified WLTP cycle, and the results indicate that the selected components can meet the dynamic demands of light and medium-duty vehicles, achieving an estimated consumption of around 50 kWh/100 km and a driving range above 160 km. By concentrating the critical regulatory requirements within a single module, the proposed architecture facilitates multistage vehicle approval, reduces development effort and supports the scalable electrification of commercial fleets. This approach may contribute to accelerating the deployment of zero-emission vehicles in urban logistics and industrial applications, with potential benefits for both the sector and society. Full article

This research paper presents Volvo SmartCell, an AC battery technology that integrates modular multilevel converters and battery cells to form a unified system for electric vehicle propulsion and power supply. The research work addresses the broader challenge of reducing driveline cost and complexity by replacing traditional components such as inverters, onboard chargers, centralized DC/DC converters, vehicle control units and many more. SmartCell uses distributed Cluster Boards comprised of H-bridges which are controlled via wireless communication to generate AC voltage, deliver redundant low voltage power, and support cell level protection mechanisms. The prototype testing demonstrates that the system can supply traction power by engaging clusters according to the required voltage depending on motor speed, achieve AC grid charging by synthesizing sinusoidal voltages without a dedicated charger, and provide autonomous DC/DC operation through cluster level voltage regulation. Simulations further indicate that multilevel voltage generation can reduce switching losses and improve electric machine efficiency compared to conventional systems. Additional benefits include active cell balancing, support for mixed cell chemistries, and high redundancy through multiple independent power branches. Challenges remain in wireless bandwidth limitations and cost optimization of Cluster Boards. Ongoing development aims to enhance communication robustness and validate safety for non-isolated grid charging. Full article

Under high-power disturbances such as HVDC blocking, stability strategies such as generator tripping are employed to ensure the frequency stability of the sending-end power grid. For renewable energy units, rapid emergency power reduction instead of direct tripping can quickly reduce active power and suppress frequency spikes, while maintaining grid connection to provide dynamic reactive power support, avoiding voltage collapse, and smoothly restoring power after a fault, thus improving the transient stability and resilience of a high-proportion renewable energy grid. However, the control performance of rapid emergency power reduction for wind turbines is limited by the converter’s overcurrent capacity and the unit-side load limit. Sudden large-scale active power reduction can easily cause motor speed fluctuations and mechanical stress accumulation, and may trigger current limiting and protection actions when the inverter current is saturated, or the DC bus voltage exceeds the limit, thus strictly limiting the range and duration of the adjustable power. To address the engineering requirements for rapid active power reduction in wind turbines, this paper proposes a control scheme based on low-voltage ride-through (LVRT) energy dissipation circuit reuse, and simultaneously conducts a special study on LVRT reuse conditions. When the unit receives a command to rapidly reduce active power, the scheme uses a percentage current duty cycle control strategy to drive the energy-consuming circuit to quickly dissipate excess energy. Simultaneously, it controls the pitch angle to increase at the maximum adjustment rate, thus completely eliminating excess power. This scheme leverages the existing LVRT hardware of the wind turbine to expand its functionality without requiring additional equipment. Furthermore, research on LVRT reuse conditions provides crucial support for the reliable operation of the scheme, demonstrating both outstanding economic efficiency and engineering practicality. Full article

Medium voltage induction motors (MVIM) are a key component of numerous industries, such as water treatment plants, sewage discharge stations, and chilled water systems. The starting process for these MV motors is critical as it is associated with a major impact on both motor lifetime and power grid quality. In this article, a proposed modified and comprehensive starting scheme of MV three-phase induction motors driving pumps for water stations is introduced. Firstly, the starting performance and its impact on power grid quality will be discussed when all motors are normally started with direct on line connection (DOL), which is already the normal established status. A modified starting scheme based on an optimized coordination of motor starting methods in addition to variable voltage variable frequency drive (VVVFD) drive and control implementation will be discussed. A transition between the starting of variant MV induction motors as well as the starting event coordination principle will be discussed to improve the power quality relative to the obligatory time shift required for the operation. The coordination is based on an algorithm implementation which is achieved using different optimization concepts based on artificial intelligence techniques, properly conducting the transition time in addition to the power delivered by the inverter unit rather than determining the number of DOL and VVVF-implemented motors. A comparison between using the optimized VVVFD soft-starting and the proposed modified scheme is performed, focusing on the power quality improvement rather than optimizing the cost function. The modified scheme is simulated using ETAP power station for brief analysis and study of load flow rather than the complete inspection and power quality assessment. Full article

To address the low accuracy of traditional freshness detection/grading and poor adaptability to different shell colors in the duck egg industry, this study developed a non-destructive detection model and an integrated device for duck egg freshness based on machine vision combined with eggshell optical property analysis. A four-sided yolk transmission imaging system was designed, and accurate yolk region segmentation was achieved via grayscale conversion, a weighted improved Otsu algorithm for whole-egg segmentation, histogram equalization enhancement, and K-means clustering in the LAB color space. A relational model between the average four-angle yolk projected area ratio and Haugh Units (HU) freshness grades was constructed, with grading thresholds determined by constrained optimization combined with the Youden index to balance food safety and grading accuracy. Experimental results showed the model achieved an overall freshness grade discrimination accuracy of 91.3%, with a sensitivity of 97.1% and specificity of 98.9% for inedible Grade B (HU < 60) duck eggs and below. An automated testing device was further developed, adopting a roller-rotating motor collaborative mechanism for automatic flipping and imaging, and equipped with a 10 W/5500 K LED cool white light source to solve the problem of poor adaptability to different shell colors. The device achieved an overall discrimination accuracy of 88.5% with a detection time of ≤5 s per egg, and its host computer can real-time output the yolk area ratio, predicted HU value, and freshness level. This study provides a high-precision and low-cost technical solution for the refined grading of the poultry egg industry. Full article

Different types of skeletal muscle fibers display marked heterogeneity in metabolic, mechanical, and regenerative properties. However, their role in chronic musculoskeletal pain remains insufficiently integrated into clinical models. Chronic pain is associated with altered neuromuscular control, prolonged low-level activation, and reduced recruitment of high-threshold motor units. These factors may promote fiber type-specific remodeling. This narrative review critically synthesizes current evidence on the relationship between musculoskeletal pain and muscle fiber types. The focus was on metabolic vulnerability, mechanical susceptibility, and regenerative capacity. A structured literature search was conducted in PubMed, Scopus, and Web of Science, focused on human studies and key translational models. Chronic musculoskeletal pain is characterized by acquired fiber type-specific adaptations rather than a fixed unfavorable profile. In chronic pain scenarios, Type I fibers present features of chronic overload, including hypertrophy with insufficient capillarization and increased satellite cell activity. Type II fibers exhibit relative disuse, atrophy, and reduced satellite cell content, resembling accelerated muscle aging. Symptom duration, neuromuscular control strategies, and task-specific loading patterns modulate these adaptations, with interindividual variation. Muscle dysfunction in chronic pain reflects maladaptive but potentially reversible neuromuscular and histological plasticity. These findings indicate that rehabilitation strategies should be individualized, involving context-specific exercise strategies to restore muscle structure, function, and regenerative potential in chronic musculoskeletal conditions. Full article

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance electric vehicles due to their power density; however, conventional field-oriented control (FOC) relies on simplified models in which electromagnetic torque is described as a function of the quadrature current component, together with constant parameters and idealized trajectories in the $i_d-i_q$ plane, limiting adaptability and reducing efficiency and operating range under real conditions. This work introduces a flux linkage vector decomposition approach for SPMSMs, in which the permanent-magnet flux is decomposed into d- and q-axis components under core saturation and integrated into an extended field-oriented control framework. An extended FOC strategy is proposed that incorporates flux linkage vector decomposition, nonlinear magnetic saturation, cross-coupling effects, and nonlinear dependencies of electrical parameters, along with resolver angle correction and dynamic modulation index management. These enhancements modify torque and voltage trajectories by shifting the voltage-limit center and improving the definition of the MTPA, FW, and MTPV regions to better match real motor behavior, enabling performance improvements. Experimental validation on an automotive powertrain using a vehicle control unit (VCU) and precalculated lookup tables (LUTs) demonstrates improvements of up to 13.5% in low-speed torque, 13.7% in high-speed power, and efficiency gains of 4–8% across operating conditions. Full article

Background/Objectives: Preterm infants often experience impaired swallowing function, and objective assessments for this population remain limited. In this prospective single-center study, we aimed to propose and validate an automated framework that quantitatively assesses neonatal sucking behavior by tracking facial key points in bottle feeding videos. Methods: Fifty-eight preterm infants (corrected age [CA] ≤ 2 months) were enrolled, and 2 min videos of bottle-feeding were recorded. Certified therapists manually evaluated the videos using the Neonatal Oral Motor Assessment Scale (NOMAS), and an artificial intelligence (AI)-based analysis classified the videos into the following three groups: Normal, Disorganization, and Dysfunction. At 12 months CA, developmental outcomes were assessed using the Mental Development Index (MDI) and the Psychomotor Development Index (PDI) of the Bayley Scales of Infant Development, Second Edition (BSID-II). Results: Among the 58 infants, the AI-based tool correctly classified 47 and misclassified 11. The classification accuracy was 82.76 for the Normal group, 82.76 for Disorganization, and 96.55 for Dysfunction. The mean PDI was lower in the Dysfunction group than in other groups; however, the differences were not statistically significant. Conclusions: This novel AI-based video analysis demonstrates preliminary potential as a noninvasive tool for evaluating sucking behavior in preterm infants, potentially enabling early identification of dysphagia even by non-specialists in the neonatal intensive care unit (NICU) without hazard exposure. This feasibility study demonstrates preliminary technical viability of a video-based framework for neonatal sucking behavior assessment; however, further validation is required before clinical implementation. Full article

Background/Objectives: Due to their positions, professional baseball catchers are at elevated risk of concussion, which can impair visual processing. There is a need for sensitive sensorimotor monitoring tools to track concussion-related neurophysiological changes more accurately. We investigated whether oculometrics can address this need. Methods: Four Major League Baseball catchers completed an oculometric assessment shortly after suffering a concussion (Time 1) and again after completing vision rehabilitation (Time 2). The assessment produces 10 z-scored measures, including a summary score. Results: Players’ Time 1 summary score tended to be typical of a normal healthy adult (Mean = 0.07 z-scored units). On average, players improved by 1.3 z-score units from their Time 1 summary score (SD = 1.07). Exploratory analyses revealed that sensorimotor recovery was driven by smooth pursuit latency, proportion of tracking comprising smooth pursuit, and the amplitude of catch-up saccades. Conclusions: Our analysis was based on a very small sample of concussion cases, each of which was unique. Despite this limitation, our data show how oculometrics can measure improvements in visual processing following a concussion among baseball players with exceptional perceptual-motor skills. Our data highlight the risk that brain injuries in high-performing individuals go undetected due to standard-of-care tools normed to behavior from healthy control populations; for these athletes, “normal” scores cannot be interpreted as neurologically “healthy”. Full article

Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre–post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A robust dose–response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing. Full article

Ripple currents on the direct current (DC) bus in variable frequency drive (VFD) systems originate from motor load current fluctuations and the high-frequency switching of power devices. The resulting Joule heating within the DC-link capacitors is a primary driver of lifespan degradation. To address the lack of systematic models for multi-phase H-bridge inverters and the over-design caused by empirical methods, this paper proposes a novel analytical method that incorporates the 2kπ/N phase difference of parallel units for precise ripple current quantification. First, a dynamic DC-link capacitor model is established based on a single-phase H-bridge inverter, and the expressions for the instantaneous, average, and root mean square (RMS) input currents are derived. Furthermore, by introducing the 2kπ/N phase difference (where k = 0, 1, …, N − 1) among N parallel H-bridge units, a universal analytical expression for the RMS input current and its harmonic spectrum in a multi-phase system is obtained. The analysis reveals that ripple current harmonics concentrate at 2m × f_sw (where m is a positive integer and f_sw is switching frequency) and their sidebands (2m × f_sw ± f_o, f_o is output fundamental frequency), and the coupling influence of modulation index and power factor angle on ripple amplitude is quantitatively characterized. A 12 × 160 kW twelve-phase H-bridge inverter is taken as a case study, and MATLAB (v2023b) simulations and hardware experiments demonstrate that the theoretical calculations are in close agreement with the simulated and measured results, with the errors of input current harmonic amplitudes all below 5%. Compared with traditional empirical design, the proposed method reduces the capacitor volume and cost by approximately 15–20% while ensuring system reliability. This method is directly extensible to other multi-phase inverter topologies, providing a theoretical foundation for the accurate selection of DC-link capacitors. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

In amyotrophic lateral sclerosis (ALS), a central event is the withdrawal of the motor nerve terminal from its target muscle. Whether this defect is driven by faults in the motor neuron or faults that originate within the muscle remains an area of investigation. In this review, we focus on the pathological abnormalities that are found in skeletal muscle, focusing, when possible, on human ALS, with support from ALS animal models. We begin with an overview of skeletal muscle, including a review of muscle fiber type, motor units and the neuromuscular synapse. Next, we provide a description of the clinical and biomarker changes that occur in the muscles of patients with ALS. We provide an extensive account of the histopathological changes that are evident in ALS muscle, such as fiber type grouping, muscle inflammation, protein misfolding, mitochondrial dysfunction, and alterations in neuromuscular junctions and muscle satellite cells. Our review then concludes with an update of metabolic and molecular–genetic changes that are found in ALS muscle. The evidence shows that muscle can be an additional target for therapy in ALS, in combination with therapies targeting neurons and glia within the central nervous system (CNS). Full article